AudioBench: Unified Audio Benchmark

• AudioBench framework is a unified, extensible infrastructure for evaluating audio and multimodal models across a variety of tasks including speech, music, and environmental sound.
• It standardizes data sources and employs modular task suites with rigorous metrics like WER and mAP to ensure reproducible, transparent comparisons.
• The framework supports plug-and-play evaluation with open-source toolkits and automated leaderboards, driving consistent, open research in audio model benchmarking.

An AudioBench framework is a unified, extensible benchmarking and evaluation infrastructure for audio models, particularly audio LLMs (AudioLLMs) or multimodal models handling audio and related modalities. These frameworks provide systematic, multidimensional, and reproducible assessments of audio model capabilities across diverse domains such as speech understanding, environmental sound, music, and paralinguistic audio. AudioBench-style systems standardize data sources, design comprehensive task suites, provide rigorous metrics, and support open-source toolkits for evaluation and leaderboard reporting (Wang et al., 2024, Hua et al., 10 Dec 2025).

1. Design Principles and High-level Objectives

AudioBench frameworks are created with three core objectives: (1) covering the full spectrum of audio understanding, including speech, audio scene, and paralinguistic tasks; (2) providing open-source, extensible toolkits for plug-and-play model evaluation; (3) maintaining automated leaderboards for transparent and reproducible comparison. The universal approach arises from the lack of prior unified benchmarks for AudioLLMs, which must support open-ended instruction following, discriminative classification, and robust scoring on genuine audio input (Wang et al., 2024). Central principles are modularity (configurable datasets/tasks), coverage (speech, sound, music), reproducibility (versioned data/configs), transparency (public protocols), and extensibility (user-registered models/tasks).

2. Task Suite Taxonomy and Dataset Integration

AudioBench frameworks employ a task taxonomy that spans multiple audio understanding dimensions. For example, (Wang et al., 2024) defines:

• Speech Understanding: ASR (Automatic Speech Recognition), SQA (Speech-driven Question Answering), SI (Speech Instruction Following)
• Audio Scene Understanding: AQA (Audio Question Answering), AC (Audio Captioning)
• Voice/Paralinguistic Understanding: ER (Emotion Recognition), AR (Accent Recognition), GR (Gender Recognition)

Datasets are selected or newly created to ensure domain diversity (speech, environmental, music), language breadth, and coverage of both discriminative and generative evaluation settings. For instance, AudioBench includes 26 datasets (7 designed for the benchmark), mixing standard sources (LibriSpeech, CommonVoice, AudioCaps, IEMOCAP, VoxCeleb1) with newly synthesized, multilingual, and paralinguistic datasets. Each task is paired with curated splits (eval/test) to prevent leakages and enable held-out benchmarking (Wang et al., 2024).

3. Evaluation Metrics and Scoring Protocols

AudioBench frameworks formalize evaluation through standardized discriminative and generative metrics, allowing comparison across highly heterogeneous tasks:

• Accuracy: For single-label classification (e.g., AR, GR): $Accuracy = \frac{TP + TN}{TP + TN + FP + FN}$
• Precision, Recall, F$_1$ Score: Applied to multi-class or multi-label settings
• Mean Average Precision (mAP): $mAP = \frac{1}{C}\sum_{c=1}^{C}AP_c$
• Word Error Rate (WER): $WER = \frac{S + D + I}{N}$ (substitutions, deletions, insertions over $N$ reference words)
• Generative QA/Judged Generation: For open-ended output (e.g., instruction following, captioning), model outputs are scored via a "model-as-judge" paradigm. An LLM (e.g., Llama-3-70B-Instruct or GPT-4) assesses outputs against detailed rubrics, producing either a discrete (e.g., $0$–$5$) or continuous ($1$–$10$) score (Wang et al., 2024, Yang et al., 2024). There is strong empirical validation that LLM-based judgments correlate with expert human scoring.

Benchmarks such as AIR-Bench further refine this protocol by (a) using model reference outputs and LLM-based consistent scoring with positional bias mitigation (choice/randomized ordering); (b) computing specialized metrics for complex, mixed-modality generative chat tasks (Yang et al., 2024).

4. Reference Implementations and Software Architecture

An AudioBench-style framework releases an open-source Python toolkit composed of (at minimum):

• Data Loaders: Module for dataset normalization, reproducible splits, metadata management
• Prompt Templates: Diverse, task-specific prompts (≥20 per class to probe instruction robustness)
• Model Interface: Abstract wrapper class for inference on (audio, prompt) pairs; supports arbitrary user backends
• Metric Suite: WER, accuracy, F$_1$, METEOR, and model-as-judge utilities
• Evaluation Workflow: End-to-end script for running evaluations and generating per-task/result summaries (CSV, JSON)
• Leaderboard Infrastructure: Automated tools to package results, submit them, ensure protocol compliance, and publication via web interfaces (typically FastAPI backends and SQL/NoSQL stores) (Wang et al., 2024, Hua et al., 10 Dec 2025)

Best-practice implementation details include: chunked inference for long-form inputs, systematic configuration for hardware, prompt randomization for robustness analysis, and checksum/signature validation for leaderboard submissions.

5. Empirical Insights, Model Landscape, and Limitations

AudioBench-based evaluations reveal that no single AudioLLM or cascade currently dominates all tasks—specialists (e.g., Whisper+Llama3) achieve low WER on ASR but may underperform on paralinguistic or scene understanding, while end-to-end AudioLLMs achieve moderate overall scores but exhibit sensitivity to prompt wording and instruction format (Wang et al., 2024, Yang et al., 2024). Key findings include:

• Performance trade-offs linked to pretraining data distribution and architecture
• Strong speech-specialized capabilities are not predictive of broader audio scene or paralinguistic performance
• Prompt sensitivity yields up to 10% WER variance within the same model (e.g., SALMONN)
• No end-to-end AudioLLM matches ASR-augmented cascades on speech QA, but such cascades fail on tasks not captured in the transcription domain
• Free-form generation quality lags on mixed audio (music+speech, background sounds)

A core implication is that generalized audio-LLMs require significantly broader instruction tuning, semi-supervised data for non-speech domains, and evaluative protocols beyond classic classification (Wang et al., 2024, Yang et al., 2024).

6. Extensibility and Future Directions

AudioBench frameworks are engineered for extensibility—new tasks, languages, and evaluation metrics can be registered via lightweight YAML/JSON configuration without core code changes (Wang et al., 2024, Hua et al., 10 Dec 2025). Recommended trajectories include:

• Incorporation of long-form and hierarchical audio sequences for complex meeting/event summarization
• Expansion to dialogic and mixed-modal (audio+text+image) rounds for context tracking and reasoning
• Deeper multilingual and code-switching evaluation, incorporating non-English and dialectal data (see UltraEval-Audio’s 10-language and Chinese comprehension benchmarks) (Shi et al., 4 Jan 2026)
• Movement beyond human-LLM-as-judge, toward reference-free or perceptually grounded automatic metrics
• Integration with downstream deployment constraints (latency, hardware, robustness)

Systematic adherence to transparent, open, and version-controlled protocols positions AudioBench frameworks as reference standards in audio model benchmarking and leaderboard-driven reproducible research.

7. Practical Adoption and Impact

Adoption of AudioBench-style frameworks is facilitated by comprehensive documentation, standardized data bundles, lightweight model-wrapping protocols, one-command evaluation scripts, and integrated web-based reporting. The capacity to extend with user-specific tasks—such as domain-specific medical sound detection or wildlife audio monitoring—ensures applicability across academic, clinical, and industrial settings (Wang et al., 2024, Yang et al., 2024). Leaderboards maintained by these frameworks serve to catalyze rapid iteration and transparent cross-institutional comparison, driving progress in foundational audio model research.

References

• "AudioBench: A Universal Benchmark for Audio LLMs" (Wang et al., 2024)
• "VABench: A Comprehensive Benchmark for Audio-Video Generation" (Hua et al., 10 Dec 2025)
• "AIR-Bench: Benchmarking Large Audio-LLMs via Generative Comprehension" (Yang et al., 2024)
• "UltraEval-Audio: A Unified Framework for Comprehensive Evaluation of Audio Foundation Models" (Shi et al., 4 Jan 2026)